Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.
We consider the problem of energy aware localized routing in ad hoc networks. In localized routing algorithms, each node forwards a message based on the position information about itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we extend our previous work on randomized localized routing algorithms that achieve high packet delivery rates and show that they have good overall power consumption. We present two different variants of energy aware randomized routing, namely "greedy" and "compass", and we study their performance using different cost metrics (e.g., forwarding power, remaining node energy, or a combination of both). We study their performance experimentally on different topologies and compare it with other existing algorithms. Our simulation results show that energy aware randomized algorithms achieve superior packet delivery rates and moderate energy consumption.
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